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Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security by Chen, Xinyun (author);Wang, Wenxiao (author);Bender, Chris (author);Ding, Yiming (author);Jia, Ruoxi (author);Li, Bo (author);Song, Dawn (author) is a nonfiction available to read on EtoBox.

What is Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security about?

Training deep neural networks from scratch could be computationally expensive and requires a lot of training data. Recent work has explored different watermarking techniques to protect the pretrained deep neural networks from potential copyright infringements. However, these techniques could be vulnerable to watermark removal attacks. In this work, we propose REFIT, a unified watermark removal framework based on fine-tuning, which does not rely on the knowledge of the watermarks, and is effective against a wide range of watermarking schemes. In particular, we conduct a comprehensive study of a realistic attack scenario where the adversary has limited training data, which has not been emphasized in prior work on attacks against watermarking schemes. To effectively remove the watermarks without compromising the model functionality under this weak threat model, we propose two techniques that are incorporated into our fine-tuning framework: (1) an adaption of the elastic weight consolidation (EWC) algorithm, which is originally proposed for mitigating the catastrophic forgetting phenomenon; and (2) unlabeled data augmentation (AU), where we leverage auxiliary unlabeled data from other

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Author

Chen, Xinyun (author);Wang, Wenxiao (author);Bender, Chris (author);Ding, Yiming (author);Jia, Ruoxi (author);Li, Bo (author);Song, Dawn (author)

Publisher

ACM

Published

2021

Language

EN

Category

nonfiction